Satellites which photograph planetary surfaces generate vast amounts of data. Such satellites include both Earth monitoring satellites and satellites orbiting other bodies in the solar system. Typically, the number of researchers available to process the satellite data is relatively small. As a result, only a fraction of the available data gets processed, meaning that many important discoveries might remain hidden.
One way in which the problem of processing large amounts of image data has been tackled is using crowdsourcing. Crowdsourcing is the process of obtaining needed services, ideas, or content by soliciting contributions from a large group of people, and especially from an online community. For example, the Zooniverse web portal hosts a variety of projects which use online volunteers to process scientific data. Once such project is Galaxy Zoo, which shows volunteers images of galaxies obtained by space telescopes and asks them to classify the objects shown in the images. The original Galaxy Zoo project ran for two years, and during that time more than 150,000 people contributed, allowing the 900,000 galaxies imaged by the Sloan Digital Sky Survey to be classified in months, rather than the years it would have taken using traditional methods.
For many applications it would be desirable not only to locate and/or classify a feature in an image, but also to be able to determine the real-world location of that feature. For example, when a natural disaster has occurred, satellite images showing the damage quickly become available. If the geographical locations (i.e. accurate latitude and longitude values) of features shown in the satellite images (e.g. collapsed buildings, broken bridges, fallen trees, etc.) could rapidly be determined, emergency services could be provided with accurate and up-to date maps of the disaster region which would greatly assist in the relief effort.
It is therefore desirable to have a fast and accurate system for determining the geographical location of any given point in a satellite image. Such as system could advantageously be used in conjunction with a crowdsourcing platform to rapidly identify features of a specified type in the image data and generate a map showing the geographical locations of the identified features.